Competitive Knowledge Transfer–Enhanced Surrogate-Assisted Search for Production Optimization

Abstract

Production optimization is a crucial component of closed-loop reservoir management, which typically aims to search for the best development scheme for maximum economic benefit. Over the decades, a large body of algorithms have been proposed to address production optimization problems, among which the surrogate-assisted evolutionary algorithm (SAEA) gained much research popularity due to its problem information-agnostic implementation and strong global search capability. However, existing production optimization methods often optimize individual tasks from scratch in an isolated manner, ignoring the available optimization experience hidden in previously optimized tasks. The incapability of transferring knowledge from possibly related tasks makes these algorithms always require a considerable number of simulation runs to obtain high-quality development schemes, which could be computationally prohibitive. To address this issue, this paper proposes a novel competitive knowledge transfer (CKT) method to leverage the knowledge from previously solved tasks toward enhanced production optimization performance. The proposed method consists of two parts: (1) similarity measurement that uses both reservoir features and optimization data for identifying the most promising previously solved task and (2) CKT that launches a competition between the development schemes of different tasks to decide whether to trigger the knowledge transfer. The efficacy of the proposed method is validated on a number of synthetic benchmark functions as well as two production optimization tasks. The experimental results demonstrate that the proposed method can significantly improve production optimization performance and achieve better optimization results when certain helpful previously optimized tasks are available.